Skill Development: Steps to become a Data Scientist

The world of data science is booming rapidly in this modern era. As of October 2020, there are 93,500 data scientist job openings in India alone. So, if you dream of becoming a data scientist, you have landed on the right page.

In this blog, we are going to cover the steps on how to become a data scientist along with an introduction to data science.

What is Data Science?

According to Wikipedia, “Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structural and unstructured data. Data science is related to data mining, machine learning, and big data.”

Who is a Data Scientist?

A data scientist is someone who collects, analyses, and interprets big amounts of data. Learning data science will open many doors to be at the top of your career. 

Roles and responsibilities of a Data Scientist?

• Analyzing ample amounts of information to identify trends and patterns.
• Create predictive models and machine-learning algorithms.
• Create solutions and strategies for business challenges.
• Coordinate with engineering and product development teams.
• Spot valuable data sources and automate collection processes.
• Clean, organize data.

Skills required to be a Data Scientist

• Knowledge of statistics, algorithms, mathematics, and machine learning.
• Knowledge of programming languages like Python, SQL, SAS, R, and Hive.
• Analytical skills.
• Understanding of business and the ability to frame the right questions to ask, and find answers in the available data.
• Communication skills.

The average salary of a Data Scientist

The average salary for senior-level – ₹1,700,000 PA

The average salary for entry-level – ₹500,000 PA.

Steps to Become a Data Scientist

1. Master skills in Algebra, Statistics, and Machine Learning

A data scientist needs to be better at statistics than a software engineer and at the same time be better at software engineering than a statistician. 

2. Learn to handle Big Data

A big amount of segregated and non-segregated data is handled by data scientists. The data analysis cannot be performed using a single tool. So, data scientists use Hadoop, MapReduce, or Spark, which are big data software to achieve distributed processing.  To learn more about big data, there are many online courses you could join.

3. Knowledge of Databases

Database management software like MySQL or Cassandra is used to store and analyze data by most industries due to the huge amount of data generated virtually every minute. Knowledge of the DBMS will help you in the process.

4. Knowledge of Coding

The language in which data communication is done is something that every data scientist should know. To be a great data scientist, you must also be a good coder.

5. Knowledge of Data Munging, Visualization, and Reporting

The process of converting the raw form of data into a form that is easy to learn, analyze, and visualize is called data munging. The visualization of data and its presentation are important for a data scientist as he relies heavily on them when facilitating managerial and administrative decisions using data analysis.

6. Try Real Projects

After familiarizing yourself with the theory, you now must work on real projects. You need to keep practicing data science projects. Google is the perfect place to find resources. 

7. Keep up with Data Science Communities for the latest trends and updates

You need to connect with people and communities in your field in order to get insights into the latest trends and be on the lookout for new job opportunities.

We hope the above-mentioned information helps you in securing your dream job as a data scientist.